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Project:
Description: This is a winning submission from the 2024 AI Data Readiness Challenge.
DESCRIPTION:

This is a winning submission from the 2024 AI Data Readiness Challenge.

IMPACT: Explores the AI Data Readiness of CRDC data.
LEVEL OF DOCUMENTATION: Moderate
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Project:
Description: This is a winning submission from the 2024 AI Data Readiness Challenge.
DESCRIPTION:

This is a winning submission from the 2024 AI Data Readiness Challenge.

IMPACT: Explores the AI Data Readiness of CRDC data.
LEVEL OF DOCUMENTATION: Moderate
resource icon RELATED Models
Project:
Description: This is a winning submission from the 2024 AI Data Readiness Challenge.
DESCRIPTION:

This is a winning submission from the 2024 AI Data Readiness Challenge.

IMPACT: Explores the AI Data Readiness of CRDC data.
LEVEL OF DOCUMENTATION: Moderate
resource icon RELATED Models
Project:
Description: This is a winning submission from the 2024 AI Data Readiness Challenge.
DESCRIPTION:

This is a winning submission from the 2024 AI Data Readiness Challenge.

IMPACT: Explores the AI Data Readiness of CRDC data.
LEVEL OF DOCUMENTATION: Moderate
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Project: ADMIRRAL
Description: The P2B1 capability is an autoencoder that determines a set of features to describe molecular dynamics (MD) simulation data most efficiently.
DESCRIPTION:

The P2B1 capability is an autoencoder that determines a set of features to describe molecular dynamics (MD) simulation data most efficiently.

IMPACT: Used to generate a tractable set of features from a larger input dataset that can then be fed into additional models for a variety of purposes.
INPUT DATA TYPE: Molecular Dynamic Simulations
INPUT DATA FORMAT: Unspecified
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB
https://github.com/CBIIT/NCI-DOE-Collab-Pilot2-Autoencoder_MD_Simulation_Data
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Project: ATOM
Description: The Cancer Kinase Selectivity resource contains datasets and models for disease target identification.
DESCRIPTION:

The Cancer Kinase Selectivity resource contains datasets and models for disease target identification.

IMPACT: Enables disease target identification.
INPUT DATA FORMAT: Unspecified
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB
https://github.com/ATOMconsortium/AMPL
Project: Cellular-Level Pilot
Description: A human-data-trained model for canine primary tumor prediction using gene expression data.
DESCRIPTION:

A human-data-trained model for canine primary tumor prediction using gene expression data.

IMPACT: Enables identification of primary tumor types in misclassified or outlier samples in canine oncological datasets.
PRIMARY PUBLICATION: cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine
INPUT DATA TYPE: Gene Expression
INPUT DATA FORMAT: Tabular
LEVEL OF DOCUMENTATION: Moderate
AVAILABLE ON GITHUB
https://github.com/CBIIT/CTULIP
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Project: Cellular-Level Pilot
Description: Predicts combinations of drug responses under different experimental configurations.
DESCRIPTION:

Predicts combinations of drug responses under different experimental configurations.

IMPACT: Enables predictions of drug responses under different experimental configurations.
PRIMARY PUBLICATION: Predicting Tumor Cell Line Response to Drug Pairs with Deep Learning
INPUT DATA TYPE: Drug Molecular Descriptors, Gene Expression
INPUT DATA FORMAT: Tabular
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB
https://github.com/CBIIT/NCI-DOE-Colab-Pilot1-Combo-combination-drug-response-p…
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Project: IMPROVE
Description: This is a community model for drug response prediction curated as part of the IMPROVE project.
DESCRIPTION:

This is a community model for drug response prediction curated as part of the IMPROVE project.

IMPACT: Enables prediction of drug response with drug and cancer features.
INPUT DATA TYPE: Gene Expression, Copy Number Variation, Drug SMILES, Protein-Protein Interaction
INPUT DATA FORMAT: Tabular
LEVEL OF DOCUMENTATION: Moderate
AVAILABLE ON GITHUB
https://github.com/JDACS4C-IMPROVE/DualGCN
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Project: IMPROVE
Description: This is a community model for drug response prediction curated as part of the IMPROVE project.
DESCRIPTION:

This is a community model for drug response prediction curated as part of the IMPROVE project.

IMPACT: Enables prediction of drug response with drug and cancer features. 
INPUT DATA TYPE: Gene Expression, Mutation, Copy Number Variation, Drug Fingerprints, Drug Target
INPUT DATA FORMAT: Tabular
LEVEL OF DOCUMENTATION: Moderate
AVAILABLE ON GITHUB
https://github.com/JDACS4C-IMPROVE/PathDSP
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